scCART-Heatmap Dotplot
Mercedes Guerrero-Murillo
mguerrero@carrerasresearch.org2023-06-30 13:31:25.371242
# Set the working directory to the project folder.
project_folder <- "."
setwd(project_folder)library(Seurat)
library(ggplot2)
library(ggalluvial)
library(grid)
library(forcats)
library(see)
library(dplyr)
library(gghalves)
library(viridis)
library(stringr)
library(kableExtra)cols2_2 <- c(`0 CD8+ Eff mem (EM)` = "#A6CEE3", `1 CD8+ Eff cytotox (Ecyt)` = "#1F78B4",
`2 Early prolif: HMGN+/HMGB+/PCNA+ cells` = "#B2DF8A", `3 CD4+ Naive/SCM` = "#33A02C",
`4 Early prolif: MCM3/5/7+ PCNA+ cells` = "#FB9A99", `5 Late prolif: histones enriched MKI67+ cells` = "#E31A1C",
`6 CD4+ Central/Effector memory (CM/EM)` = "#FDBF6F", `7 Ribosomal/Mitocondrial/Degradated cells` = "#FF7F00",
`8 Late prolif: CDK+/CDC+/AURKA+ MIK67+ cells` = "#CAB2D6", `9 γδ Tcells` = "#6A3D9A")Top10 genes
integrated.obj <- readRDS("integrated.obj.rds")
markers <- FindAllMarkers(integrated.obj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
markers %>%
group_by(cluster) %>%
top_n(n = 10, wt = avg_log2FC) -> top10
DoHeatmap(subset(integrated.obj, downsample = 1000), features = unique(top10$gene),
group.colors = cols2_2) + NoLegend()
DotPlot(integrated.obj, features = unique(top10$gene), cols = c("#0610C1", "#C2062F",
"#C2062F")) + coord_flip() + RotatedAxis()
FeaturePlot(integrated.obj, features = unique(top10$gene))
Genes of interest
Genes from the DEG, Pablo selected the most important ones.
pablo_markers <- c("CCR7", "IL7R", "TCF7", "CD27", "SELL", "GZMK", "GZMA", "GNLY",
"GZMH", "NKG7", "GZMB", "PRF1", "MCM7", "PCNA", "CD4", "CD8A", "CD8B", "CD95",
"MKI67", "CDK1", "GZMM", "HIST1H4C", "HIST1H3B", "HIST1H1B", "HIST1H3C", "HIST1H2BF",
"HIST1H1E", "HIST1H3F", "HIST1H2AG", "HIST1H2AL", "HIST1H2BH", "MTRNR2L12", "MTRNR2L8",
"RPS2", "CCNB1", "CCNA2", "CCNA1", "CDC20", "HMGN2", "TRDV1", "TRGV8")
DoHeatmap(subset(integrated.obj, downsample = 1000), features = unique(pablo_markers)) +
NoLegend()
DotPlot(integrated.obj, features = unique(pablo_markers), cols = c("#0610C1", "#C2062F",
"#C2062F")) + coord_flip() + RotatedAxis()Gamma delta genes
gd_markers <- c("ZFP36", "CCL4", "PRF1", "GNLY", "NKG7", "KLRF1", "FCGR3A", "TRGV8",
"TRDV1", "TYROBP", "GZMA", "GZMB", "GZMH", "GZMK", "GZMM", "TRDV1", "TRGV8")
DoHeatmap(subset(integrated.obj, downsample = 1000), features = unique(gd_markers)) +
NoLegend()
DoHeatmap(subset(integrated.obj, downsample = 1000), features = unique(gd_markers))
DotPlot(integrated.obj, features = unique(gd_markers), cols = c("#0610C1", "#C2062F",
"#C2062F")) + coord_flip() + RotatedAxis()
Session Info
sessionInfo()## R version 4.3.0 (2023-04-21)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.4.1
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Madrid
## tzcode source: internal
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] kableExtra_1.3.4 stringr_1.5.0 viridis_0.6.3 viridisLite_0.4.2
## [5] gghalves_0.1.4 dplyr_1.1.2 see_0.7.5 forcats_1.0.0
## [9] ggalluvial_0.12.5 ggplot2_3.4.2 SeuratObject_4.1.3 Seurat_4.3.0
## [13] knitr_1.43
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 rstudioapi_0.14 jsonlite_1.8.4
## [4] magrittr_2.0.3 spatstat.utils_3.0-3 farver_2.1.1
## [7] rmarkdown_2.21 vctrs_0.6.2 ROCR_1.0-11
## [10] spatstat.explore_3.2-1 webshot_0.5.4 htmltools_0.5.5
## [13] sass_0.4.6 sctransform_0.3.5 parallelly_1.35.0
## [16] KernSmooth_2.23-21 bslib_0.4.2 htmlwidgets_1.6.2
## [19] ica_1.0-3 plyr_1.8.8 plotly_4.10.1
## [22] zoo_1.8-12 cachem_1.0.8 igraph_1.4.2
## [25] mime_0.12 lifecycle_1.0.3 pkgconfig_2.0.3
## [28] Matrix_1.5-4 R6_2.5.1 fastmap_1.1.1
## [31] fitdistrplus_1.1-11 future_1.32.0 shiny_1.7.4
## [34] digest_0.6.31 colorspace_2.1-0 patchwork_1.1.2
## [37] tensor_1.5 irlba_2.3.5.1 labeling_0.4.2
## [40] progressr_0.13.0 fansi_1.0.4 spatstat.sparse_3.0-1
## [43] httr_1.4.6 polyclip_1.10-4 abind_1.4-5
## [46] compiler_4.3.0 withr_2.5.0 highr_0.10
## [49] MASS_7.3-60 tools_4.3.0 lmtest_0.9-40
## [52] httpuv_1.6.11 future.apply_1.11.0 goftest_1.2-3
## [55] glue_1.6.2 nlme_3.1-162 promises_1.2.0.1
## [58] Rtsne_0.16 cluster_2.1.4 reshape2_1.4.4
## [61] generics_0.1.3 gtable_0.3.3 spatstat.data_3.0-1
## [64] tidyr_1.3.0 data.table_1.14.8 sp_1.6-0
## [67] xml2_1.3.4 utf8_1.2.3 spatstat.geom_3.2-1
## [70] RcppAnnoy_0.0.20 ggrepel_0.9.3 RANN_2.6.1
## [73] pillar_1.9.0 limma_3.56.1 later_1.3.1
## [76] splines_4.3.0 lattice_0.21-8 survival_3.5-5
## [79] deldir_1.0-6 tidyselect_1.2.0 miniUI_0.1.1.1
## [82] pbapply_1.7-0 gridExtra_2.3 svglite_2.1.1
## [85] scattermore_1.1 xfun_0.39 matrixStats_0.63.0
## [88] stringi_1.7.12 lazyeval_0.2.2 yaml_2.3.7
## [91] evaluate_0.21 codetools_0.2-19 tibble_3.2.1
## [94] cli_3.6.1 uwot_0.1.14 xtable_1.8-4
## [97] reticulate_1.28 systemfonts_1.0.4 munsell_0.5.0
## [100] jquerylib_0.1.4 Rcpp_1.0.10 globals_0.16.2
## [103] spatstat.random_3.1-5 png_0.1-8 parallel_4.3.0
## [106] ellipsis_0.3.2 listenv_0.9.0 scales_1.2.1
## [109] ggridges_0.5.4 leiden_0.4.3 purrr_1.0.1
## [112] rlang_1.1.1 cowplot_1.1.1 rvest_1.0.3
## [115] formatR_1.14